Instructions to use nlpaueb/sec-bert-num with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nlpaueb/sec-bert-num with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="nlpaueb/sec-bert-num")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("nlpaueb/sec-bert-num") model = AutoModelForPreTraining.from_pretrained("nlpaueb/sec-bert-num", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from nlpaueb/sec-bert-num: direct link, hf CLI and curl.
- Browser
- Download file 439 MB
-
https://huggingface.co/nlpaueb/sec-bert-num/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://nlpaueb/sec-bert-num/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/nlpaueb/sec-bert-num/resolve/main/pytorch_model.bin
439 MB
- Xet hash:
- fd1e23881a33ca0550f42ff85064ea61212f205de9ec4215735868bceca04cbf
- Size of remote file:
- 439 MB
- SHA256:
- 037164e15fe4eee630f50915b5b5a2a13b22b8deca1e37f6487a30fc4e0618c1
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